{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sympy import *\n",
    "from sympy.abc import x,y,c,C,pi,mu\n",
    "#from sympy.plotting import plot_implicit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4 \\\\mu \\\\pi'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latex(4*pi*mu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$4 \\pi \\mu_{0}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex=Eq(1/(sqrt((x+c)**2+y**2))+\n",
    "      1/(sqrt((x-c)**2+y**2)),C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex2=ex.subs({'c':2 ,'C':2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7223b65f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p1=plot_implicit(ex2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7223f4d0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KS5NGj5b++Ef/B/r279+vMWPG6MEHH9SpAR8AWVEhvfaadMstgV72OxYtWqROnTopIyNDK1ascFfIiYh2SZ/xZOn0hg0bzOmnn25SUlJMSkqKadGihenRo4f54osvAq8lN9cYB5c9xuOPP26GDBliDhw44LoUY4wxNTXGPPpoMMvNa5dZ1y6RjWaZdWPk5xtzzz3GHD0at0s0ypEjR8ywYcNMbm5u4Nc+cMA+J/btC/zS33HXXXeZ7t27m5SUFNO5c2fTpk0bk5OT47aoKDM24QL5+1JSUkxxcbGz68+caUxZmbPLmyVLlphIJGJ2797trojvmTbNmBdfDOZaVVVV5qyzzjL9+/c3lZWVJi0tzRQUFMT1mmvXGnPbbTaAfFJTU2NuuOEGM2nSpMCvXVlpzAMPGFNSEvil6/TGG2+YK664wnUZxrCXRTDuvNPOr/34YzfXv/XWW1VRUaHs7Gylp6fr5ptvdlOIpM2bpZtuksaOla65Jphr1i6z3rJlS2DLrAcOlO69184cWb48rpdqkFWrVmn+/Plavny50tPTlZ6erpdffjnu1y0ulh56SPr1r9lioLFYqRcDxtiZFz162P7l5ujNN+2etzfe6OZk7nis1KvP0aPSc8/ZmQRXXWU3Ym9uNm+2fcYTJybOgh9HohrYIJBjaN066Z13pOuvtycsNwd799oXoyFDbMvRFReBXGv7drtHx8iRdpC3OZw0cvSoPQi2Sxfp8stdV5MQCGQXqqqkp56SunWTsrNdVxM/hw5Jf/mL1LKlXfDhunXkMpBrPf20tGaNdNdd9hy5pur99+2K1Zyc5tPwiAEC2aWPPpLeeEMaMSKYqXFBqaiQnn9eOnDAvhPwZVtFHwJZsucF/vvf0rZt0q9+JXlwWEXMlJVJL75o52VfconrahIOgexaVZX07rs2nLOyEjuY9+2Tnn3WBnJOjl0g4xNfArnWoUPS3Ll2dd+oUfFZpRiU4mLbT9yunTR0qD2RGw1GIPtk1Srp88/tOW1DhriuJnqrVtm3qKecIo0b52bALhq+BfK3LVhg99Lu0SO42SexsGmT3a0tOdk2KJrjoGUMEcg++uQTKT/f7pFw4YW2r9knxtiTMtaulaqr7ROxVy9br898DmTJPq47d0qvv27fZfTvb1+YW7VyXdl3lZXZgenSUrvkPSOjeQxSBoBA9lllpfThh3baUPv29i1tr15u3g7u3SutWGFP1k5Ksl0rGRn+hUVdfA/kb6uutlME339fOvVU270xdKgdCAy6FVpZabcA+PRT2/992mnSj39Mt0QcEMiJZOdOG861W3u2bGn3ko3H7qLr19tAqB2Qq6mR0tP9Op6qoRIpkL+vtNSG8/799udeUWF/FoMHx34v4ZISacsW2xKW7DjHeefZrjTE1po1azR+/HitXr1abdq0OUXSaknXGmMKjvc5BLLHNm60/c61/bbG2BZU69bSwYO2b++Hpptt3mwHYE4+2W4Kn5pqn4TdutkWcJ8+UteuwX4v8ZbIgfxD9u2zP8fSUhuevXrZ7q7UVNvCLiyUvtpp8xg7d37Twt2zx77w1p78nJwsDRoUyLcASb///e91+PBh5ebm/l1SkTGmzg3LCeQEVVVlp54dz8knu58bHKSmFsh1qamRysuP//HWrd3tQ4zvOnLkiAYNGqQNGzaslvR/xpjquu7v7fabqNtJJ9n+PjQ/SUn87BPFnj17tH//fklqJ6m1pDqaUQm6QT0AJIKJEydq2rRpkvRPSffXd38CGQDi4IknnlA4HNb1118vSfdJGhQKhS6r63PoQ0aT0Jz6kJGQopr2RgsZADxBIAOAJwhkAPAEgQwAniCQAcATBDIAeIJABgBPEMgA4AkCGQA8QSADgCcIZHhlypQp6tu3r9LS0jRq1Cjt3bvXdUlAYAhkeCU7O1sFBQXasGGD+vTpo5kz69zPG2hSCGR4ZdiwYQp/dW7RkCFDVFRU5LgiIDgEMrw1d+5cjRgx4rgfz8vLU2ZmpjIzM1VcXBxgZUB8sP0mApeVlaVdu3Yd8//Tp0/X1Vdf/fXf8/PztWDBAoWiOIee7Tfhuai23+QIJwTutddeq/Pj8+bN06JFi/T6669HFcZAU0EgwytLly7V/fffr5UrV6pt27auywECRZcFvJKamqrKykp17NhRkh3Ye+SRR+r9PLos4Dm6LJB4tm7d6roEwBlmWQCAJwhkAPAEgQwAniCQAcATBDIAeIJABgBPEMgA4AkCGQA8QSADgCcIZADwBIEMAJ4gkAHAEwQyAHiCQAYATxDIAOAJAhkAPEEgA4AnCGQA8ASBDACeIJABwBMEMgB4gkAGAE8QyADgCQIZADxBIAOAJwhkAPAEgQwAniCQAcATBDIAeIJABgBPEMgA4AkCGQA8QSADgCcIZADwBIEMb82ePVuhUEglJSWuSwECQSDDS9u3b9err76qnj17ui4FCAyBDC/dfvvtmjVrlkKhkOtSgMAQyPDOwoUL1b17dw0YMMB1KUCgwq4LQPOUlZWlXbt2HfP/06dP14wZM7Rs2bJ6v0ZeXp7y8vIkScXFxTGvEQhayBjTkPs36M5AQ33wwQf66U9/qrZt20qSioqK1K1bN61evVpdunQ57udlZmYqPz8/qDKBhoqq740WMrxy3nnnaffu3V//+8wzz1R+fr6Sk5MdVgUEgz5kAPAELWR4rbCw0HUJQGBoIQOAJwhkAPAEgQwAniCQAcATBDIAeIJABgBPEMgA4AkCGQA8QSADgCcIZADwBIEMAJ4gkAHAEwQyAHiCQAYATxDIAOAJAhkAPNHQM/UAL4VCoaXGmOGu6wAag0AGAE/QZQEAniCQAcATBDIAeIJABgBPEMgA4AkCGQA8QSADgCcIZADwBIEMAJ74f6lvc/W6KvhdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7223f84e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7223f925c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7223f798d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7223f84128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7225087320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7223b65e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7223f75f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7223f2d320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7223b73390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sympy.plotting.plot.Plot at 0x7223b65898>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(10,20):\n",
    "    ex3=ex.subs({'c':2 ,'C':i/10})\n",
    "    p1.extend(plot_implicit(ex3))\n",
    "p1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wDR8+XBs3blRtrbk4DPsxU88nKiulNWuk++/3uhI7MVOvYf/8pzR6tJSa6nUlcY+ZerEkMdH8Uu3a5XUl8IsTJ8x6KISxfxDIPnLTTdLrr0t1dV5XAj9Yu1aaM8frKhAOAtlnpk6V4nTeA8JQWCgNHMgu035DIPtM377S6dPNr3GB+HXihPTWW2ZSEfyFQPahu+6S8vKk6mqvK4GNnnlGmj8/fsYdxxIC2ad+/nPpySfNgH/gjLVrpQkT6KrwKwLZpzp3liZNktav97oS2KKwULroIslnC5zhOwhkHxs0SGrXzvQXIr4dOCDt3Gl2mIEdCgsLNXToUFVXV8txnAscx/mv4ziDm3oMgexzU6dKZWXSZ595XQm8UlkpFRSYfmPYIzMzU1OnTtWjjz4qSUskrXFdd09TjyGQY8Ctt0obNkjFxV5XgmirqJBWrDAXemGf3/3ud9q8ebMkZciEcpMI5BixYIH0xhvSvn1eV4JoOXFCWr1aeuABqVMnr6tBQyoqKlRVVSVJnSR1aO58AjmGzJkjbdki7WnyTRFiQXm5aRnPm2cWn4Kd5s+frz/84Q+S9Kykxc2dTyDHEMeRfvUr6ZNPpK1bva4GkbJ7t/Tii9LChQxvs9kzzzyjQCCgO+64Q5Iek5TpOM4NTT2G1d5i1JtvmlbULbd4XUl0xMtqb9u2SceOSbNmeV0JwsRqb/Fs7FgzLC43VzJdWPAz15WefdZ8TBjHLgI5hg0caPqVV6yQvvjC62rwY1VVSYsXS5MnS9df73U1iCS6LOJAKCS9/LL5d9o0KWDtToo/Xqx2WWzbZoYz3nEHF+98ji4LGG3aSNOnS2PGmC4MVoqzX0WF9Ne/St26md2iCeP4QAs5Dm3cKB0/LmVnS23bel1N64ilFvKmTebC3W23xea7mTjVohYy/91xKCvL/MI/9ZR0zTXSkCFeVwRJ+vJLM5xt4kSzOwziD10WcapbNzNmua7OvDXmop93TpwwXUmffGLWo+jf3+uK4BW6LCDJtMy++ca0zLp08bqa8Pmxy6KmRvr3v00f/9Sp9BPHOLos0HLTp5vF7jdtkr7+WsrMlC67zOuqYtPRo2YmZUKCdPPNrEOB/0cg46wzAeG6Zk2MoiJp6FBpcJMruKKliould96REhOlmTNj54IqWg+BjHM4jrmwJEm7dknPPSddcok0bpwJ7Uh66KGH9PLLL6tdu3bq16+fVq5cqc6dO0f2m0aQ60rvvmvCuG9fM54YaAx9yGiR8nLpvfekU6ekYcPMtOxIKCgo0A033KBAIKDf/va3kqTFi5tdJMu6PuQvvzQ7uYRC0ujRUr9+XlcEj9GHjNaTkmJm+YVCZmLJCy+Yt9x9+5oujdba4XjSpElnPx4zZoxeeOGF1vnCUXDwoBkpUVUlpaVJM2bQLYHwEMgIS5s20oAB5pBMAOXnSydPms01Bw1qvckMTz/9tG699dZG78/NzVVubq4kKRgMts43DUN9vdk6a9cu87z07m3Wm2itP06IP3RZoNWUlkoffywdOSIlJ5tRGn36nHvehAkTVF5efs7tixYt0rRp085+XFRUpPXr18txmn+3F60ui2DQBHBVldShg9S9u+nCAZpBlwWiq1cvc0hmwsm+fab1XFtrxtj26CH17Clt2bKlya+zatUq5eXlaevWrS0K40g5dUr66itzQe7IETM+OylJuvZas9s30NoIZERE27am++LMxb9QyCyYs2+fWUfDccwIhK5dpc6dTVh36iTl5+dr8eLFev3119WxY8eo1PrNN2ZscFmZGYPtOObo0MF0w1xzDZM2EB10WcBzn31mRiW0aydNn56mUKhGF17YTW3aSGlpY/T73/9NrmvWd25sEkVmZoYKC8/tsqivlwoLTcBWVJgp49XV0rffmtvODOPr0UNKTWUxH0QMXRbwh0svNYckffllw2uD1tVJH37Y+Nc4dcpMZGnI5ZebVjhgOwIZvtC2rZnO3ZgLLmj6fsAPWO0NACxBIAOAJQhkALAEgQwAliCQAcASBDIAWIJABgBLEMgAYAkCGQAsQSADgCUIZACwBIEMAJYgkAHAEgQyAFiCQAYASxDIAGAJAhkALEEgA4AlCGQAsASBDACWIJABwBIEMgBYgkAGAEsQyABgCQIZACxBIAOAJQhkALAEgQwAliCQAcASBDIAWIJABgBLEMgAYAkCGQAsQSDDWkuXLpXjODp69KjXpQBRQSDDSiUlJdq8ebN69+7tdSlA1BDIsNLChQu1ZMkSOY7jdSlA1BDIsM6GDRvUs2dPDRs2zOtSgKgKeF0A4tOECRNUXl5+zu2LFi1STk6OCgoKmv0aubm5ys3NlSQFg8FWrxGINsd13XDOD+tkIFy7d+/W+PHj1bFjR0lSaWmpevTooR07diglJaXRx2VkZKioqChaZQLhalHfGy1kWGXIkCE6cuTI2c/79u2roqIiJSUleVgVEB30IQOAJWghw2qHDh3yugQgamghA4AlCGQAsASBDACWIJABwBIEMgBYgkAGAEsQyABgCQIZACxBIAOAJQhkALAEgQwAliCQAcASBDIAWIJABgBLEMgAYAkCGQAsEe6eeoCVHMfJd113std1AOeDQAYAS9BlAQCWIJABwBIEMgBYgkAGAEsQyABgCQIZACxBIAOAJQhkALAEgQwAlvg/ddzd9nMnumIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7224056630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p1=plot_implicit(Eq(x**2+y**2,5))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
